Accounting for regional background and population size in the detection of spatial clusters and outliers using geostatistical filtering and spatial neutral models: the case of lung cancer in Long Island, New York.
Accounting for regional background and population size in the detection of spatial clusters and outliers using geostatistical filtering and spatial neutral models: the case of lung cancer in Long Island, New York.
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DOI：
10.1186/1476-072x-3-14
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发表时间：
2004-07-23
影响因子：
4.9
通讯作者：
Jacquez, Geoffrey M
中科院分区：
文献类型：
--
作者：
Goovaerts, Pierre;Jacquez, Geoffrey M
背景技术背景：完全空间随机性（CSR）是许多空间模式统计检验（如局部聚类或边界分析）所采用的零假设。然而，企业社会责任是不是一个相关的零假设高度复杂和有组织的系统，如在环境和健康科学中遇到的潜在的空间格局是本。本文提出了一种地统计学的方法来过滤空间变化的人口规模所造成的噪声，并产生空间相关的中性模型，占区域背景的地统计学平滑所观察到的死亡率。这些中性模型与当地Moran统计数据结合使用，以确定拿骚、皇后区和萨福克县（纽约，美国）男性和女性肺癌地理分布的空间聚类和离群值。研究结果：我们开发了一种中性模型的类型学，逐步放松零假设的假设，允许存在空间自相关，非均匀风险，并纳入空间异质的人口规模。与以前的研究相比，空间自相关的引入导致更少的显著邮政编码，证实了早期的说法，即CSR可能导致过度识别显著的空间聚类或离群值的数量。通过地统计过滤来考虑总体大小增加了聚类的大小，同时去除了大多数空间离群值。将区域背景整合到中性模型中产生了显著不同的空间聚类和离群值，从而确定了SMR值显着偏离其区域背景的邮政编码。结论：本文提出的方法使研究人员能够评估地理关系，使用适当的零假设，占现实世界中的系统中存在的背景变化。特别是，这种新的方法允许人们识别超出背景变化的地理模式。在空间统计软件中采用这一方法将有助于发现死亡率的空间差异，确定有针对性的癌症控制干预措施的理由，包括考虑保健服务需求，以及为筛查和诊断测试分配资源。它将允许研究人员系统地评估他们的结果对替代零假设下隐含的假设的敏感性。
BACKGROUND: Complete Spatial Randomness (CSR) is the null hypothesis employed by many statistical tests for spatial pattern, such as local cluster or boundary analysis. CSR is however not a relevant null hypothesis for highly complex and organized systems such as those encountered in the environmental and health sciences in which underlying spatial pattern is present. This paper presents a geostatistical approach to filter the noise caused by spatially varying population size and to generate spatially correlated neutral models that account for regional background obtained by geostatistical smoothing of observed mortality rates. These neutral models were used in conjunction with the local Moran statistics to identify spatial clusters and outliers in the geographical distribution of male and female lung cancer in Nassau, Queens, and Suffolk counties, New York, USA. RESULTS: We developed a typology of neutral models that progressively relaxes the assumptions of null hypotheses, allowing for the presence of spatial autocorrelation, non-uniform risk, and incorporation of spatially heterogeneous population sizes. Incorporation of spatial autocorrelation led to fewer significant ZIP codes than found in previous studies, confirming earlier claims that CSR can lead to over-identification of the number of significant spatial clusters or outliers. Accounting for population size through geostatistical filtering increased the size of clusters while removing most of the spatial outliers. Integration of regional background into the neutral models yielded substantially different spatial clusters and outliers, leading to the identification of ZIP codes where SMR values significantly depart from their regional background. CONCLUSION: The approach presented in this paper enables researchers to assess geographic relationships using appropriate null hypotheses that account for the background variation extant in real-world systems. In particular, this new methodology allows one to identify geographic pattern above and beyond background variation. The implementation of this approach in spatial statistical software will facilitate the detection of spatial disparities in mortality rates, establishing the rationale for targeted cancer control interventions, including consideration of health services needs, and resource allocation for screening and diagnostic testing. It will allow researchers to systematically evaluate how sensitive their results are to assumptions implicit under alternative null hypotheses.